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<p>While deep learning techniques, especially image classification using deep learning, continue to evolve, it has been noted that there is a large time gap in applying these techniques in geological studies. Recently, a new architecture called the vision transformer (ViT), which is an alternative to convolutional neural networks (CNN), has attracted considerable attention. In addition, it has been proposed that the pre-training of classification models using mathematically generated images instead of real images, called formula-driven supervised learning (FDSL), achieves a comparative or even higher performance in visual understanding. In this study, we applied these new techniques to the classification of microfossils (radiolarians). Compared with a previous CNN model, the ViT-based model achieved 6–8% higher average precision. On average, the precision of the FDSL pre-trained models was slightly higher than that of the models pre-trained on real images. Therefore, we propose that these techniques may be suitable for image classification in geological tasks.</p>

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Classifying microfossil radiolarians on fractal pre-trained vision transformers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Kazuhide Mimura,
  • Takuya Itaki,
  • Hirokatsu Kataoka,
  • Ayumu Miyakawa

摘要

While deep learning techniques, especially image classification using deep learning, continue to evolve, it has been noted that there is a large time gap in applying these techniques in geological studies. Recently, a new architecture called the vision transformer (ViT), which is an alternative to convolutional neural networks (CNN), has attracted considerable attention. In addition, it has been proposed that the pre-training of classification models using mathematically generated images instead of real images, called formula-driven supervised learning (FDSL), achieves a comparative or even higher performance in visual understanding. In this study, we applied these new techniques to the classification of microfossils (radiolarians). Compared with a previous CNN model, the ViT-based model achieved 6–8% higher average precision. On average, the precision of the FDSL pre-trained models was slightly higher than that of the models pre-trained on real images. Therefore, we propose that these techniques may be suitable for image classification in geological tasks.